You are using local mode, --executor-memory won't take effect for local mode, please use other cluster mode.
On Thu, Jun 16, 2016 at 9:32 AM, Jeff Zhang <zjf...@gmail.com> wrote: > Specify --executor-memory in your spark-submit command. > > > > On Thu, Jun 16, 2016 at 9:01 AM, spR <data.smar...@gmail.com> wrote: > >> Thank you. Can you pls tell How to increase the executor memory? >> >> >> >> On Wed, Jun 15, 2016 at 5:59 PM, Jeff Zhang <zjf...@gmail.com> wrote: >> >>> >>> Caused by: java.lang.OutOfMemoryError: GC overhead limit exceeded >>> >>> >>> It is OOM on the executor. Please try to increase executor memory. >>> "--executor-memory" >>> >>> >>> >>> >>> >>> On Thu, Jun 16, 2016 at 8:54 AM, spR <data.smar...@gmail.com> wrote: >>> >>>> Hey, >>>> >>>> error trace - >>>> >>>> hey, >>>> >>>> >>>> error trace - >>>> >>>> >>>> ---------------------------------------------------------------------------Py4JJavaError >>>> Traceback (most recent call >>>> last)<ipython-input-22-925883e4d630> in <module>()----> 1 temp.take(2) >>>> >>>> /Users/my/Documents/My_Study_folder/spark-1.6.1/python/pyspark/sql/dataframe.pyc >>>> in take(self, num) 304 with SCCallSiteSync(self._sc) as css: >>>> 305 port = >>>> self._sc._jvm.org.apache.spark.sql.execution.EvaluatePython.takeAndServe(--> >>>> 306 self._jdf, num) 307 return >>>> list(_load_from_socket(port, BatchedSerializer(PickleSerializer()))) >>>> 308 >>>> >>>> /Users/my/Documents/My_Study_folder/spark-1.6.1/python/lib/py4j-0.9-src.zip/py4j/java_gateway.py >>>> in __call__(self, *args) 811 answer = >>>> self.gateway_client.send_command(command) 812 return_value = >>>> get_return_value(--> 813 answer, self.gateway_client, >>>> self.target_id, self.name) 814 >>>> 815 for temp_arg in temp_args: >>>> >>>> /Users/my/Documents/My_Study_folder/spark-1.6.1/python/pyspark/sql/utils.pyc >>>> in deco(*a, **kw) 43 def deco(*a, **kw): 44 try:---> >>>> 45 return f(*a, **kw) 46 except >>>> py4j.protocol.Py4JJavaError as e: 47 s = >>>> e.java_exception.toString() >>>> /Users/my/Documents/My_Study_folder/spark-1.6.1/python/lib/py4j-0.9-src.zip/py4j/protocol.py >>>> in get_return_value(answer, gateway_client, target_id, name) 306 >>>> raise Py4JJavaError( 307 "An error >>>> occurred while calling {0}{1}{2}.\n".--> 308 >>>> format(target_id, ".", name), value) 309 else: >>>> 310 raise Py4JError( >>>> Py4JJavaError: An error occurred while calling >>>> z:org.apache.spark.sql.execution.EvaluatePython.takeAndServe. >>>> : org.apache.spark.SparkException: Job aborted due to stage failure: Task >>>> 0 in stage 3.0 failed 1 times, most recent failure: Lost task 0.0 in stage >>>> 3.0 (TID 76, localhost): java.lang.OutOfMemoryError: GC overhead limit >>>> exceeded >>>> at com.mysql.jdbc.MysqlIO.nextRowFast(MysqlIO.java:2205) >>>> at com.mysql.jdbc.MysqlIO.nextRow(MysqlIO.java:1984) >>>> at com.mysql.jdbc.MysqlIO.readSingleRowSet(MysqlIO.java:3403) >>>> at com.mysql.jdbc.MysqlIO.getResultSet(MysqlIO.java:470) >>>> at com.mysql.jdbc.MysqlIO.readResultsForQueryOrUpdate(MysqlIO.java:3105) >>>> at com.mysql.jdbc.MysqlIO.readAllResults(MysqlIO.java:2336) >>>> at com.mysql.jdbc.MysqlIO.sqlQueryDirect(MysqlIO.java:2729) >>>> at com.mysql.jdbc.ConnectionImpl.execSQL(ConnectionImpl.java:2549) >>>> at >>>> com.mysql.jdbc.PreparedStatement.executeInternal(PreparedStatement.java:1861) >>>> at >>>> com.mysql.jdbc.PreparedStatement.executeQuery(PreparedStatement.java:1962) >>>> at >>>> org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD$$anon$1.<init>(JDBCRDD.scala:363) >>>> at >>>> org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD.compute(JDBCRDD.scala:339) >>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306) >>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270) >>>> at >>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306) >>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270) >>>> at >>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306) >>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270) >>>> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66) >>>> at org.apache.spark.scheduler.Task.run(Task.scala:89) >>>> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214) >>>> at >>>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) >>>> at >>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) >>>> at java.lang.Thread.run(Thread.java:745) >>>> >>>> Driver stacktrace: >>>> at org.apache.spark.scheduler.DAGScheduler.org >>>> <http://org.apache.spark.scheduler.dagscheduler.org/>$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431) >>>> at >>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419) >>>> at >>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418) >>>> at >>>> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) >>>> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) >>>> at >>>> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418) >>>> at >>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799) >>>> at >>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799) >>>> at scala.Option.foreach(Option.scala:236) >>>> at >>>> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799) >>>> at >>>> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640) >>>> at >>>> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599) >>>> at >>>> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588) >>>> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48) >>>> at >>>> org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620) >>>> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832) >>>> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845) >>>> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858) >>>> at >>>> org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:212) >>>> at >>>> org.apache.spark.sql.execution.EvaluatePython$$anonfun$takeAndServe$1.apply$mcI$sp(python.scala:126) >>>> at >>>> org.apache.spark.sql.execution.EvaluatePython$$anonfun$takeAndServe$1.apply(python.scala:124) >>>> at >>>> org.apache.spark.sql.execution.EvaluatePython$$anonfun$takeAndServe$1.apply(python.scala:124) >>>> at >>>> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:56) >>>> at >>>> org.apache.spark.sql.DataFrame.withNewExecutionId(DataFrame.scala:2086) >>>> at >>>> org.apache.spark.sql.execution.EvaluatePython$.takeAndServe(python.scala:124) >>>> at >>>> org.apache.spark.sql.execution.EvaluatePython.takeAndServe(python.scala) >>>> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) >>>> at >>>> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) >>>> at >>>> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) >>>> at java.lang.reflect.Method.invoke(Method.java:498) >>>> at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231) >>>> at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381) >>>> at py4j.Gateway.invoke(Gateway.java:259) >>>> at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133) >>>> at py4j.commands.CallCommand.execute(CallCommand.java:79) >>>> at py4j.GatewayConnection.run(GatewayConnection.java:209) >>>> at java.lang.Thread.run(Thread.java:745) >>>> Caused by: java.lang.OutOfMemoryError: GC overhead limit exceeded >>>> at com.mysql.jdbc.MysqlIO.nextRowFast(MysqlIO.java:2205) >>>> at com.mysql.jdbc.MysqlIO.nextRow(MysqlIO.java:1984) >>>> at com.mysql.jdbc.MysqlIO.readSingleRowSet(MysqlIO.java:3403) >>>> at com.mysql.jdbc.MysqlIO.getResultSet(MysqlIO.java:470) >>>> at com.mysql.jdbc.MysqlIO.readResultsForQueryOrUpdate(MysqlIO.java:3105) >>>> at com.mysql.jdbc.MysqlIO.readAllResults(MysqlIO.java:2336) >>>> at com.mysql.jdbc.MysqlIO.sqlQueryDirect(MysqlIO.java:2729) >>>> at com.mysql.jdbc.ConnectionImpl.execSQL(ConnectionImpl.java:2549) >>>> at >>>> com.mysql.jdbc.PreparedStatement.executeInternal(PreparedStatement.java:1861) >>>> at >>>> com.mysql.jdbc.PreparedStatement.executeQuery(PreparedStatement.java:1962) >>>> at >>>> org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD$$anon$1.<init>(JDBCRDD.scala:363) >>>> at >>>> org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD.compute(JDBCRDD.scala:339) >>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306) >>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270) >>>> at >>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306) >>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270) >>>> at >>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306) >>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270) >>>> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66) >>>> at org.apache.spark.scheduler.Task.run(Task.scala:89) >>>> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214) >>>> at >>>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) >>>> at >>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) >>>> ... 1 more >>>> >>>> >>>> >>>> On Wed, Jun 15, 2016 at 5:39 PM, Jeff Zhang <zjf...@gmail.com> wrote: >>>> >>>>> Could you paste the full stacktrace ? >>>>> >>>>> On Thu, Jun 16, 2016 at 7:24 AM, spR <data.smar...@gmail.com> wrote: >>>>> >>>>>> Hi, >>>>>> I am getting this error while executing a query using sqlcontext.sql >>>>>> >>>>>> The table has around 2.5 gb of data to be scanned. >>>>>> >>>>>> First I get out of memory exception. But I have 16 gb of ram >>>>>> >>>>>> Then my notebook dies and I get below error >>>>>> >>>>>> Py4JNetworkError: An error occurred while trying to connect to the Java >>>>>> server >>>>>> >>>>>> >>>>>> Thank You >>>>>> >>>>> >>>>> >>>>> >>>>> -- >>>>> Best Regards >>>>> >>>>> Jeff Zhang >>>>> >>>> >>>> >>> >>> >>> -- >>> Best Regards >>> >>> Jeff Zhang >>> >> >> > > > -- > Best Regards > > Jeff Zhang > -- Best Regards Jeff Zhang